Parameters use to fit the meta-analytic estimator.
Parameters use to fit the corrector.
The following figure provides an interactive window to explore the meta-analytic map in detail.
This panel shows the the corrrected meta-analytic map.
| X | Y | Z | Peak Stat | Cluster Size (mm3) | ||
|---|---|---|---|---|---|---|
| Tail | Cluster ID | |||||
| Positive | 1 | 0.00 | -58.00 | 30.00 | 10.31 | 12360 |
| 1a | -2.00 | -52.00 | 22.00 | 7.61 | ||
| 1b | 2.00 | -52.00 | 48.00 | 2.75 | ||
| 1c | -10.00 | -52.00 | 18.00 | 2.46 | ||
| 2 | -48.00 | -62.00 | 24.00 | 8.47 | 46480 | |
| 2a | -52.00 | -56.00 | 18.00 | 7.39 | ||
| 2b | -46.00 | -58.00 | 32.00 | 7.17 | ||
| 2c | -46.00 | -70.00 | 12.00 | 6.27 | ||
| 3 | 54.00 | -44.00 | 12.00 | 7.61 | 32632 | |
| 3a | 58.00 | -48.00 | 18.00 | 6.95 | ||
| 3b | 46.00 | -68.00 | 6.00 | 6.27 | ||
| 3c | 50.00 | -52.00 | 20.00 | 6.04 | ||
| 4 | -2.00 | 56.00 | 22.00 | 7.17 | 35496 | |
| 4a | 0.00 | 16.00 | 52.00 | 6.95 | ||
| 4b | -4.00 | 8.00 | 54.00 | 6.50 | ||
| 4c | 2.00 | 20.00 | 48.00 | 5.81 | ||
| 5 | -42.00 | 20.00 | -8.00 | 6.50 | 9952 | |
| 5a | -46.00 | 26.00 | -14.00 | 6.27 | ||
| 5b | -36.00 | 18.00 | 0.00 | 6.04 | ||
| 5c | -50.00 | 32.00 | -10.00 | 5.81 | ||
| 6 | 56.00 | -2.00 | -16.00 | 6.27 | 8536 | |
| 6a | 54.00 | 2.00 | -24.00 | 5.09 | ||
| 6b | 54.00 | -16.00 | -12.00 | 3.31 | ||
| 6c | 48.00 | -10.00 | -18.00 | 3.03 | ||
| 7 | 48.00 | 16.00 | 32.00 | 5.33 | 17160 | |
| 7a | 38.00 | 24.00 | -6.00 | 4.86 | ||
| 7b | 48.00 | 26.00 | 0.00 | 4.86 | ||
| 7c | 52.00 | 26.00 | 8.00 | 4.61 | ||
| 8 | -42.00 | 6.00 | 48.00 | 4.36 | 8696 | |
| 8a | -48.00 | 32.00 | 18.00 | 3.58 | ||
| 8b | -50.00 | 28.00 | 26.00 | 3.58 | ||
| 8c | -40.00 | 6.00 | 34.00 | 3.58 | ||
| 9 | 16.00 | -26.00 | -2.00 | 4.11 | 1984 | |
| 9a | 22.00 | -30.00 | -6.00 | 2.75 | ||
| 9b | 10.00 | -18.00 | 4.00 | 2.18 | ||
| 9c | 22.00 | -26.00 | -4.00 | 2.18 | ||
| 10 | 14.00 | 10.00 | 0.00 | 3.58 | 2000 | |
| 10a | 12.00 | 16.00 | -6.00 | 3.03 | ||
| 10b | 12.00 | 4.00 | 4.00 | 3.03 | ||
| 10c | 16.00 | 6.00 | -4.00 | 3.03 | ||
| 11 | 36.00 | -54.00 | 44.00 | 3.31 | 2192 | |
| 11a | 24.00 | -66.00 | 58.00 | 2.75 | ||
| 11b | 32.00 | -62.00 | 48.00 | 2.75 | ||
| 11c | 40.00 | -60.00 | 46.00 | 2.75 | ||
| 12 | -26.00 | -4.00 | 56.00 | 3.31 | 960 | |
| 12a | -32.00 | -8.00 | 52.00 | 2.46 | ||
| 13 | -24.00 | -28.00 | -2.00 | 3.31 | 904 | |
| 14 | -10.00 | 10.00 | 0.00 | 3.03 | 1296 | |
| 14a | -14.00 | 4.00 | -6.00 | 2.75 | ||
| 14b | -14.00 | 2.00 | 6.00 | 2.75 | ||
| 14c | -6.00 | 10.00 | 0.00 | 2.75 | ||
| 15 | -6.00 | -84.00 | -6.00 | 2.75 | 464 | |
| 15a | -6.00 | -90.00 | 0.00 | 2.46 | ||
| 15b | -2.00 | -82.00 | -2.00 | 2.46 | ||
| 15c | -10.00 | -82.00 | -8.00 | 2.18 | ||
| 16 | -10.00 | -88.00 | 10.00 | 2.75 | 104 | |
| 17 | 26.00 | -6.00 | -20.00 | 2.75 | 368 | |
| 18 | -54.00 | 6.00 | 20.00 | 2.46 | 1056 | |
| 18a | -52.00 | 6.00 | 12.00 | 2.46 | ||
| 18b | -52.00 | 8.00 | 20.00 | 2.46 | ||
| 18c | -52.00 | 10.00 | 12.00 | 2.46 | ||
| 19 | -22.00 | -68.00 | 48.00 | 2.46 | 240 | |
| 20 | 38.00 | -34.00 | 48.00 | 2.46 | 280 | |
| 20a | 44.00 | -32.00 | 58.00 | 2.46 | ||
| 20b | 42.00 | -30.00 | 48.00 | 2.18 | ||
| 21 | -34.00 | 14.00 | 44.00 | 2.18 | 80 | |
| 22 | -12.00 | -54.00 | 10.00 | 2.18 | 168 | |
| 23 | -30.00 | -56.00 | -20.00 | 2.18 | 96 | |
| 24 | -20.00 | -78.00 | -34.00 | 1.88 | 104 |
The FocusCounter analysis characterizes the relative contribution of each experiment in a meta-analysis to the resulting clusters by counting the number of peaks from each experiment that fall within each significant cluster.
The heatmap presents the relative contributions of each experiment to each cluster in the thresholded map. There is one row for each experiment, and one column for each cluster, with column names being PostiveTail/NegativeTail indicating the sign (+/-) of the cluster's statistical values. The rows and columns were re-ordered to form clusters in the heatmap.
We kindly ask to report results preprocessed with this tool using the following boilerplate.
A multilevel kernel density (MKDA) meta-analysis \citep{wager2007meta} was performed was performed
with NiMARE 0.6.0+6.g55f7ea1 (RRID:SCR_017398; \citealt{Salo2023}), using a(n) MKDA kernel. An MKDA
kernel \citep{wager2007meta} was used to generate study-wise modeled activation maps from
coordinates. In this kernel method, each coordinate is convolved with a sphere with a radius of
10.0 and a value of 1. For voxels with overlapping spheres, the maximum value was retained. Summary
statistics (OF values) were converted to p-values using an approximate null distribution. The input
dataset included 3296 foci from 405 experiments. False discovery rate correction was performed with
the Benjamini-Hochberg procedure \citep{benjamini1995controlling}.
@article{Salo2023,
doi = {10.52294/001c.87681},
url = {https://doi.org/10.52294/001c.87681},
year = {2023},
volume = {3},
pages = {1 - 32},
author = {Taylor Salo and Tal Yarkoni and Thomas E. Nichols and Jean-Baptiste Poline and Murat Bilgel and Katherine L. Bottenhorn and Dorota Jarecka and James D. Kent and Adam Kimbler and Dylan M. Nielson and Kendra M. Oudyk and Julio A. Peraza and Alexandre Pérez and Puck C. Reeders and Julio A. Yanes and Angela R. Laird},
title = {NiMARE: Neuroimaging Meta-Analysis Research Environment},
journal = {Aperture Neuro}
}
@article{benjamini1995controlling,
title={Controlling the false discovery rate: a practical and powerful approach to multiple testing},
author={Benjamini, Yoav and Hochberg, Yosef},
journal={Journal of the Royal statistical society: series B (Methodological)},
volume={57},
number={1},
pages={289--300},
year={1995},
publisher={Wiley Online Library},
url={https://doi.org/10.1111/j.2517-6161.1995.tb02031.x},
doi={10.1111/j.2517-6161.1995.tb02031.x}
}
@article{wager2007meta,
title={Meta-analysis of functional neuroimaging data: current and future directions},
author={Wager, Tor D and Lindquist, Martin and Kaplan, Lauren},
journal={Social cognitive and affective neuroscience},
volume={2},
number={2},
pages={150--158},
year={2007},
publisher={Oxford University Press},
url={https://doi.org/10.1093/scan/nsm015},
doi={10.1093/scan/nsm015}
}